code: def backward(ctx, *flat_grads):
      fw_args = ctx.saved_tensors
      fw_mapped_args = fw_args[: ctx._num_mapped_args]
      pos_args = fw_args[ctx._num_mapped_args :]
  
      grads = map_impl(
          ctx._joint_graph,
          fw_mapped_args + flat_grads,
          pos_args,
      )
      return None, None, None, *grads
  
imports: from contextlib import nullcontext
  import torch
  import torch.utils._pytree
  from torch._C import DispatchKey
  from torch._dispatch.python import suspend_functionalization
  from torch._functorch.aot_autograd import AOTConfig, create_joint, from_fun
  from torch._higher_order_ops.cond import _has_potential_branch_input_alias, _has_potential_branch_input_mutation, UnsupportedAliasMutationException
  from torch._ops import HigherOrderOperator
  from torch._subclasses.fake_tensor import FakeTensorMode
  from torch._subclasses.functional_tensor import FunctionalTensor, FunctionalTensorMode, unset_functional_temporarily
  from torch.fx.experimental.proxy_tensor import disable_proxy_modes_tracing, make_fx, ProxyTorchDispatchMode, track_tensor_tree
  from torch.multiprocessing.reductions import StorageWeakRef
label_desc: Use image node_id 2 for calling the MapAutogradOp obj's underlying member method code with example usage: obj.backward(ctx) and returns: None,  None,  None,  unknown